Do a quantile plot on the bimodal distribution fits.
This plots the theoretical and actual data quantiles to allow the user to examine the adequacy of two gld distributions mixture fit.
qqplot.gld.bi(data, fit, param1, param2, len = 10000, name = "", corner = "topleft",type="",range=c(0,1),xlab="",main="")
data |
Data fitted. |
fit |
Parameters of distribution fit. |
param1 |
Can be either |
param2 |
Can be either |
len |
Precision of the quantile calculatons. Default is 10000. This means 10000 points are taken from 0 to 1. |
name |
Name of the data set, added to the title of plot if |
corner |
Can be |
type |
This can be "" or "str.qqplot", the first produces the raw quantiles and the second plot them on a straight line. Default is "". |
range |
This is the range for which the quantiles are to be plotted.
Default is |
xlab |
x axis label, if left blank, then default is "Data" |
main |
Title of the plot, if left blank, a default title will be added. |
A plot is given.
Steve Su
# set.seed(1000)
# junk<-rweibull(300,3,2)
## Fitting mixture of generalised lambda distributions on the data set using
## both the maximum likelihood and partition maximum likelihood and plot the
## resulting fits
# junk<-fun.auto.bimodal.ml(faithful[,1],per.of.mix=0.1,clustering.m=clara,
# init1.sel="rprs",init2.sel="rmfmkl",init1=c(-1.5,1.5),init2=c(-0.25,1.5),
# leap1=3,leap2=3)
# fun.plot.fit.bm(nclass=50,fit.obj=junk,data=faithful[,1],
# name="Maximum likelihood using",xlab="faithful1",param.vec=c("rs","fmkl"))
## Do a quantile plot on the raw quantiles
# qqplot.gld.bi(faithful[,1],junk$par,param1="rs",param2="fmkl",
# name="RS FMKL ML fit")
## Or a qq plot to examine deviation from straight line
# qqplot.gld.bi(faithful[,1],junk$par,param1="rs",param2="fmkl",
# name="RS FMKL ML fit",type="str.qqplot")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.